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Bridging the gap between synthetic and real-world motion prediction, this framework achieves superior performance by leveraging objectness priors to refine motion labels.
Ditch the heavy embedding sync: this federated recommendation method slashes communication costs by transmitting lightweight cluster labels instead of full item embeddings, without sacrificing accuracy.